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What’s Next For Watson Mobile Apps?

IBM’s quest to build Watson into a business (see our story here) took another step last week when the vendor announced the winners of its 2014 Watson Mobile Developer Challenge to create consumer and business apps with its cognitive computing capabilities.

Among the winners, GenieMD aims to help consumers ask in natural language form general health questions (such as what are the signs of a stroke), or make more specific queries related to their own health parameters. It incorporates data it knows about an individual from its personal health management records system, medication tracker and integration with wearable health devices for a holistic view of the patient, and also has provided Watson with over 2,000 leaflets of health conditions and drug information to serve as a corpus of relevant data.

So, if a user asks if he can take aspirin for a headache, the system knows from its personal health records management capabilities that the user takes warfarin, while Watson uses what it has learned about health conditions to connect the dots that Warfarin and aspirin don’t mix, and that a health provider should be consulted before making such a move. The mobile app also is targeted to be used by physicians to help them validate diagnosis and as a recommendation engine to suggest health improvement tips during periodic evaluations of an individual’s health.

Majestyk’s Friendly Anthropomorphic Networked Genome (Fang) wants to help kids learn via an unconventional user experience – and capitalize on the $4.4 trillion global education market. The prototype in the video showcasing the winners displays a stuffed critter of some sort, with a Cookie Monster-like voice, plopped atop a tablet – the idea being to provide a multi-sensory, touch and talk educational experience – answering a child’s question about why his birthday comes only once a year. Says Majestyk product designer John Paul Benini in the video, “what’s cool about working with Watson is that we can capture a child’s question on the front end, break it down, feed it in to the deep QA back-end and pull back a proper formatted English response.”

Parents will be able to have dashboard-insight into how kids interact with the device, providing “comprehension levels across topics and metrics such as the number of words a child has been exposed to,” not to mention “invaluable feedback on the direction each child is predisposed to succeed.” Hey, it’s a competitive world, so you can’t start to figure these things out too soon!

Red Ant, meanwhile, prototypes a mobile sales training app designed to discreetly turn retail store employees into experts on the products they’re selling and the customers they are selling them to. “With Watson we wanted to bring the power of Big Data to the shop floor,” said alex sbardella head of product during his video presentation of the SellSmart system. A big part of the effort is combining the structured data with which the retail world is so familiar with unstructured data that, the company says, Watson really excels at.

The solution uses over 11,000 HTML files to build a corpus to make Watson fashionable, incorporating input from product reviews, customer data and fashion blog. “It’s not possible without the use of something like Watson to crush that [data] down into a format that is easy and quick to use during customer interactions, to make it almost invisible to the customer and not get in the way of the traditional sales process.”

Innovative Finalists

Of the 22 runners-up, six of them in health services alone. But efforts also targeted automotive research, finance, agriculture, news and cities. Ontodia was among the latter, for example, proposing an Answering People Interface City Intelligence Concierge service that lets users ask in-depth questions about cities, starting with New York. It’s based on Ontodia’s existing work on PediaCities, a normalized, semantically linked data encyclopedia about New York City (see our story here).

The service, says Ontodia co-founder Joel Natividad, “will be for users interested in detailed, structured data about the built urban environment. We're thinking of it as a freemium service where users can ask questions for free using only current and select data. Should they require access to DeepAnswers to DeepQuestions that require historical data, then they need to have a commercial account with PediaCities,” he says. Leveraging IBM’s cognitive computing platform would advance Ontodia’s work because “it allows us to just concentrate on creating the knowledge corpus, and Watson could be the main way that users can ask about things in the corpus in a form of directed serendipity.”.

Natividad says the Ontodia team had only a week and a half to play with Watson. “So it was a mad rush of uploading select content, training Watson, and building the prototype. It was quite functional, but we only did some abbreviated training and we could have optimized our content more for Watson,” he says. “We were hoping that IBM would select us… [it just made a lot of sense given its] Open Data+Watson+Smarter Planet/Cities.” But even though Ontodia didn't make the final three, Natividad says IBM is still engaging with it to explore how to expand the prototype.